Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

I am a beginner. I have a dataset of 1700 samples with 4 features and I have to perform Hierarchical Clustering (the agglomerative version) and I need to decide whether or not to scale the data and ...

I want to use SVM in the classification layer of a 2 layer feedforward neural network. Need guidance from the community on how to approach this problem. This involves capturing the features from the ...

Let's suppose we have a random model that I can sample to generate distributions of a certain 1D variable. I want to score the distance of a test distribution to the model in question. The distance ...

I am taking this deep learning course from Andrew NG. In the 3rd lecture of 2nd week of the first course, he mentions that we can use RMSE for logistic regression as well but it will take a nonconvex ...

I would like to know if there exists an algorithm using which I would be able to extract repeating patterns from a time series dataset, provided I give it a reference shape. I have included an image ...

I have 100 regions and several features like [quality_rating, the density of stores in a region, the number of people who purchase, age, gender, income, lifestyle], etc. for each region. Let's say my ...

I'm trying to perform binary classification on a dataset with missing values. I used sklearn's iterative imputer to impute these values and I got pretty good results. However, I realized that I was ...

I am following a paper which suggests using a restricted Boltzmann machine for learning a discrete probability distribution. I have encountered a problem, however, when the scientifically "interesting"...

I want to predict wages and constructed bagged regression tree's and a random forest of regression Tree's. The bagged regression tree's outperform the random forest.
Is this result even possible , I ...

For a binary DNN, the output is $y_0 + y_1 = 1$ since they are the probability distribution, hence the sum must equate to 1. However, I've been told that $y_1$ is sufficient to represent the output of ...

I have a few models doing prediction with 4 classes, with the output precision and recall varying with different labels.
For example I have (with the class labels being 0, 1, 2, 3 on the x axis):
I ...

i have a dataset having traing.csv and testing.csv files. i have trained the model on traing data and then predict the labels for testing data. as the labels are only given in training data. there are ...

I want to develop an unsupervised learning method to identify spoken numbers in Arabic. My dataset consists of MFCC arrays. Every row consists of an array of shape(41,13), The row consists of float ...

I am working on a dataset challenge and am being asked to detect structures in the dataset.
What are some ways we can define structures within the data? Is that pretty much any patterns found? Maybe ...

If my response variable say is "has_repurchased" [0 or 1] and I have all customer level features. Can I rank the features in order of importance from the random forest model and report them as whats ...

I have a general question about asymmetric costs. In machine learning problems, there are times when the cost of a false positive is different from the cost of a false negative. Accordingly, models ...

So, I understand why simple linear or logistic regression will have infinite solutions in this case (good answers here and here). But while LASSO will only select n features, Elastic net does not have ...

As pointed out by various authors (e.g., Hastie, 2011), K-fold cross-validation has an upward bias of prediction error. I wonder whether the same holds for cross-validated effect size measures such as ...

For our ML assignment we have three datasets. The challenge is about checking whether a written and spoken number refer to the same number. We're using the MNIST dataset with handwritten numbers, and ...

One advantage of the MLP neural networks is the nonlinear transformation used on raw features. The popular ones used are the activation functions like Sigmoid, Tanh, ReLU, Leaky ReLU, etc. They are of ...

I am trying to create a NN to play a card game wherein each state is represented by the hands of 4 players. Every round, the hand of each player is decreased by 1 (discarded). Each player starts with ...

I have Weekly Units sales data of products for 2 years (104 weeks). And I am trying to forecast the Unit sales for each productid for next 8 weeks..
Please find the data image below.
note: Productid ...

I work with a chemical process in which there is a time lag between the inputs (raw material quality and cooking parameters) and the output (final product quality). The problem is that the time lag ...

I have sample records with several attributes (predictors) and a predicted variable Yes/No.
What I need is, given new data that omits the column Yes/No, to know what is the probability of Yes. Note ...

Is it possible to train a simple perceptron with a threshold activation function such as this one: https://en.wikipedia.org/wiki/Perceptron with Backpropagation instead of the perceptron rule?
is it ...

Consider the data for 3 users from the same domain:
“Design the UI”, “Develop/code the UI” and “Discuss changes with the client” are the most common tasks. The duration could be a simple average.
So,...